A new approach to service provisioning in ATM networks
IEEE/ACM Transactions on Networking (TON)
Pricing in computer networks: motivation, formulation, and example
IEEE/ACM Transactions on Networking (TON)
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 3)-Volume - Volume 3
A general preemption-based admission policy using a smart market approach
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
Pricing congestible network resources
IEEE Journal on Selected Areas in Communications
Connection establishment in high-speed networks
IEEE Journal on Selected Areas in Communications
Billing users and pricing for TCP
IEEE Journal on Selected Areas in Communications
A survey on networking games in telecommunications
Computers and Operations Research
Hidden information and actions in multi-hop wireless ad hoc networks
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
A survey on networking games in telecommunications
Computers and Operations Research
| Hi-index | 0.00 |
We consider a communication network that offers multi-class services to multiple types of traffic. Users choose service classes so as to optimize their own performance. The network associates with each traffic type a nominal service class. Optimal prices should provide incentives for the users to assign each traffic type to its nominal service class. We establish necessary and sufficient conditions for the existence of optimal prices and provide an algorithm for their computation. We indicate that optimal prices can tolerate fluctuations in the various parameters. We then devise a distributed algorithm, with which the network can compute optimal prices even when it does not have sufficient knowledge on the traffic characteristics. Next, we consider an extended model which explicitly includes congestion effects. A key factor which emerges here is the amount of traffic at the disposal of each user. We consider the typical cases of individual, social and type optimization, for which we generalize our results.